Instituto Complutense de Análisis Económico
Advances in Financial Risk Management andEconomic Policy Uncertainty: An Overview
Shawkat Hammoudeh
Lebow College of Business Drexel University
Michael McAleer
Department of Quantitative Finance National Tsing Hua University
Taiwan and
Econometric Institute Erasmus School of Economics Erasmus University Rotterdam
and Tinbergen Institute
The Netherlands and
Department of Quantitative Economics Complutense University of Madrid
Abstract
Financial risk management is difficult at the best of times, but especially so in the presence of economic uncertainty and financial crises. The purpose of this special issue on “Advances in Financial Risk Management and Economic Policy Uncertainty” is to highlight some areas of research in which novel econometric, financial econometric and empirical finance methods have contributed significantly to the analysis of financial risk management when there is economic uncertainty, especiallythe power of print: uncertainty shocks, markets, and the economy, determinants of the banking spread in the Brazilian economy: the role of micro and macroeconomic factors, forecasting value-at-risk using block structure multivariate stochastic volatility models, the time-varying causality between spot and futures crude oil prices: a regime switching approach, a regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates, a practical approach to constructing price-based funding liquidity factors, realized range volatility forecasting: dynamic features and predictive variables, modelling a latent daily tourism financial conditions index, bank ownership, financial segments and the measurement of systemic risk: an application of CoVaR, model-free volatility indexes in the financial literature: a review, robust hedging performance and volatility risk in option markets: application to Standard and Poor’s 500 and Taiwan index options, price cointegration between sovereign CDS and currency option markets in the global financial crisis, whether zombie lending should always be prevented, preferences of risk-averse and risk-seeking investors for oil spot and futures before, during and after the global financial crisis, managing financial risk in Chinese stock markets: option pricing and modeling under a multivariate threshold autoregression, managing systemic risk in The Netherlands, mean-variance portfolio methods for energy policy risk management, on robust properties of the SIML estimation of volatility under micro-market noise and random sampling, asymmetric large-scale (I)GARCH with hetero-tails, the economic fundamentals and economic policy uncertainty of Mainland China and their impacts on Taiwan and Hong Kong, prediction and simulation using simple models characterized by nonstationarity and seasonality, and volatility forecast of stock indexes by model averaging using high frequency data.
JEL Classification C58, D81, E60, G32
UNIVERSIDAD
COMPLUTENSE MADRID
Working Paper nº 1417 June, 2014
ISSN: 2341-2356 WEB DE LA COLECCIÓN: http://www.ucm.es/fundamentos-analisis-economico2/documentos-de-trabajo-del-icae Copyright © 2013, 2014 by ICAE. Working papers are in draft form and are distributed for discussion. It may not be reproduced without permission of the author/s.
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Advances in Financial Risk Management and
Economic Policy Uncertainty: An Overview*
Shawkat Hammoudeh Lebow College of Business
Drexel University
Michael McAleer Department of Quantitative Finance
National Tsing Hua University
Taiwan
and
Econometric Institute
Erasmus School of Economics
Erasmus University Rotterdam
and
Tinbergen Institute
The Netherlands
and
Department of Quantitative Economics
Complutense University of Madrid
June 2014
* The authors wish to thank the referees for their timely and helpful comments and suggestions
on the papers comprising the special issue. For financial support, the second author wishes to
acknowledge the Australian Research Council and the National Science Council, Taiwan.
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Abstract Financial risk management is difficult at the best of times, but especially so in the presence of
economic uncertainty and financial crises. The purpose of this special issue on “Advances in
Financial Risk Management and Economic Policy Uncertainty” is to highlight some areas of
research in which novel econometric, financial econometric and empirical finance methods
have contributed significantly to the analysis of financial risk management when there is
economic uncertainty, especiallythe power of print: uncertainty shocks, markets, and the
economy, determinants of the banking spread in the Brazilian economy: the role of micro and
macroeconomic factors, forecasting value-at-risk using block structure multivariate stochastic
volatility models, the time-varying causality between spot and futures crude oil prices: a
regime switching approach, a regime-dependent assessment of the information transmission
dynamics between oil prices, precious metal prices and exchange rates, a practical approach to
constructing price-based funding liquidity factors, realized range volatility forecasting:
dynamic features and predictive variables, modelling a latent daily tourism financial conditions
index, bank ownership, financial segments and the measurement of systemic risk: an
application of CoVaR, model-free volatility indexes in the financial literature: a review, robust
hedging performance and volatility risk in option markets: application to Standard and Poor’s
500 and Taiwan index options, price cointegration between sovereign CDS and currency
option markets in the global financial crisis, whether zombie lending should always be
prevented, preferences of risk-averse and risk-seeking investors for oil spot and futures before,
during and after the global financial crisis, managing financial risk in Chinese stock markets:
option pricing and modeling under a multivariate threshold autoregression, managing systemic
risk in The Netherlands, mean-variance portfolio methods for energy policy risk management,
on robust properties of the SIML estimation of volatility under micro-market noise and random
sampling, asymmetric large-scale (I)GARCH with hetero-tails, the economic fundamentals and
economic policy uncertainty of Mainland China and their impacts on Taiwan and Hong Kong,
prediction and simulation using simple models characterized by nonstationarity and
seasonality, and volatility forecast of stock indexes by model averaging using high frequency
data.
Keywords: Financial risk management, Economic policy uncertainty, Financial econometrics,
Empirical finance.
JEL: C58, D81, E60, G32.
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1. Introduction
New research and techniques in economics and finance have been used to deal with important
issues that have emerged from the 2008-09 Global Financial Crisis that started in the USA, the
sovereign debt crisis that emanated from Europe in 2010, and the resulting policy uncertainty
in the USA and worldwide. Despite the ensuing recovery, mounting financial risk and
economic policy uncertainty have confounded investors, portfolio and hedge fund managers,
and policy makers alike, especially in the USA and Europe.
Based on citations in Thomson Reuters ISI, Google Scholar, Microsoft Academic Search,
RePEc (Research Papers in Economics and Finance), and paper downloads and abstract views
in SSRN (Social Science Research Network), research papers in financial risk management and
economic policy uncertainty are among the most widely cited, downloaded and viewed articles
in finance and financial economics.
Financial risk management is difficult at the best of times, but especially so in the presence of
economic policy uncertainty. The special issue will present an extensive range of papers by
leading scholars in the field on “Advances in Financial Risk Management and Economic
Policy Uncertainty”. The purpose of the special issue is to highlight a number of areas of
research in which novel econometric, financial econometric and empirical finance methods
have contributed significantly to the analysis of financial risk management when there is
economic policy uncertainty, especifically the power of print: uncertainty shocks, markets, and
the economy (Alexopoulos and Cohen, 2014), determinants of the banking spread in the
Brazilian economy: the role of micro and macroeconomic factors (Almeida and Divino, 2014),
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forecasting value-at-risk using block structure multivariate stochastic volatility models (Asai,
Caporin and McAleer, 2014), the time-varying causality between spot and futures crude oil
prices: a regime switching approach (Balcilar, Gungor and Hammoudeh, 2014), a regime-
dependent assessment of the information transmission dynamics between oil prices, precious
metal prices and exchange rates (Balcilar, Hammoudeh and Fru Asaba, 2014), a practical
approach to constructing price-based funding liquidity factors (Bouwman, Buis, Pieterse-
Bloem and Tham, 2014), realized range volatility forecasting: dynamic features and predictive
variables (Caporin and Velo, 2014), modelling a latent daily tourism financial conditions index
(Chang, 2014), bank ownership, financial segments and the measurement of systemic risk: an
application of CoVaR (Drakos and Kouretas, 2014), model-free volatility indexes in the
financial literature: a review (Gonzalez-Perez, 2014), robust hedging performance and
volatility risk in option markets: application to Standard and Poor’s 500 and Taiwan index
options (Han, Chang, Kuo and Yu, 2014), price cointegration between sovereign CDS and
currency option markets in the financial crises of 2007-2013 (Hui and Fong, 2014), whether
zombie lending should always be prevented (Jaskowski, 2014), preferences of risk-averse and
risk-seeking investors for oil spot and futures before, during and after the global financial crisis
(Lean, McAleer and Wong, 2014), managing financial risk in Chinese stock markets: option
pricing and modeling under a multivariate threshold autoregression (Li, Ng and Chan, 2014),
managing systemic risk in The Netherlands (Liao, Sojli and Tham, 2014), mean-variance
portfolio methods for energy policy risk management (Marrero, Puch and Ramos-Real, 2014),
on robust properties of the SIML estimation of volatility under micro-market noise and random
sampling (Misaki and Kunitomo, 2014), ALRIGHT: Asymmetric LaRge-Scale (I)GARCH
with Hetero-Tails (Paolella and Polak, 2014), the economic fundamentals and economic policy
uncertainty of Mainland China and their impacts on Taiwan and Hong Kong (Sin, 2014),
prediction and simulation using simple models characterized by nonstationarity and seasonality
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(Swanson and Urbach, 2014), and volatility forecast of stock indexes by model averaging using
high frequency data (Wang and Nishiyama, 2014).
The interesting, timely and novel contributions to this special issue should highlight and
encourage innovative research in a variety of challenging areas associated with the topical and
rapidly expanding areas of financial risk management and economic policy uncertainty.
The plan of the remainder of the paper is as follows. An overview of the 22 papers is presented
in Section 2, and some final remarks are given in Section 3.
2. Overview
The first paper is “The power of print: Uncertainty shocks, markets, and the economy” by
Michelle Alexopoulos (Department of Economics, University of Toronto, Canada) and Jon
Cohen (Department of Economics, University of Toronto, Canada). There has been in recent
years a renewed interest in, and a growing recognition of, the role played by uncertainty shocks
in driving fluctuations in the economy and in asset markets. The authors create new text-based
indicators of both general economic and policy-specific uncertainty from New York Times and
use them to chart changes in the level of uncertainty in the USA for the period 1985-2007, to
determine the role of policy in these swings, and to assess their impact on the economy, equity
markets, and business cycles. Overall, the results indicate that uncertainty shocks, both general
and policy related, depress the level of economic activity, significantly increase stock market
volatility, and decrease market returns.
In the second paper, “Determinants of the banking spread in the Brazilian economy: The role
of micro and macroeconomic factors”, Fernanda Dantas Almeida (Department of Economics,
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Catholic University of Brasilia, Brazil) and Jose Angelo Divino (Department of Economics,
Catholic University of Brasilia, Brazil) use an empirical model to identify the major
determinants of the ex-post banking spread in the Brazilian economy, considering the influence
of the macroeconomic environment, specific characteristics of the financial institutions, and
elements of the banking sector. The sample consists of a balanced panel, composed of 64 banks
in the period between the first quarter of 2001 and the second quarter of 2012. The empirical
results of the static model suggest that administrative expenses, revenue from services and the
coverage index are important determinants of the ex-post spread. The
macroeconomic environment shows positive effects arising from real GDP, while the
Herfindahl-Hirschman index indicates that spreads are higher for a more concentrated banking
system. The dynamic model reveals a moderate persistence for the ex-post banking spread and
a rise in relevance for the banks’ market share. The overall pure spread indicates that banks
consider the Brazilian economy to be a risky environment in which to operate.
The third paper by Manabu Asai (Faculty of Economics, Soka University, Tokyo,
Japan), Massimiliano Caporin (Department of Economics and Management "Marco Fanno",
University of Padova, Italy), and Michael McAleer (Department of Quantitative Finance,
National Tsing Hua University, Taiwan; Econometric Institute, Erasmus School of Economics,
Erasmus University Rotterdam; Tinbergen Institute, The Netherlands) is on “Forecasting
value-at-risk using block structure multivariate stochastic volatility models”. Most multivariate
variance or volatility models suffer from a common problem, the “curse of dimensionality”.
For this reason, most are fitted under strong parametric restrictions that reduce the
interpretation and flexibility of the models. Recently, the literature has focused on multivariate
models with milder restrictions, whose purpose is to combine the need for interpretability and
efficiency faced by model users with the computational problems that may emerge when the
number of assets can be very large. A contribution to this strand of the literature including a
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block-type parameterization for multivariate stochastic volatility models is provided. The
empirical analysis on stock returns on the US market shows that 1% and 5 % Value-at-Risk
thresholds based on one-step-ahead forecasts of covariances by the new specification are
satisfactory for the period including the Global Financial Crisis.
“The time-varying causality between spot and futures crude oil prices: A regime switching
approach”, the fourth paper, is by Mehmet Balcilar (Department of Economics, Eastern
Mediterranean University, Turkey), Hasan Gungor (Department of Economics, Eastern
Mediterranean University, Turkey), and Shawkat Hammoudeh (Lebow College of Business,
Drexel University, USA). A puzzling result in the literature on the linkages between crude
oil spot and futures prices is the sensitivity of causality tests to the sample period, which has
been handled in the literature by sample splitting. In order to overcome this difficulty, the
authors propose a model that allows for time-varying Granger causality. The model is used to
investigate the time-varying causal linkages between the daily crude oil spot and futures prices
for maturities of one, two, three and four months of the West Texas Intermediate (WTI) crude
oil benchmark for the period January 2, 1986 to July 31, 2013. The empirical results indicate
that the causal links between these oil prices are strongly time varying. Both variables have
predictive power for each other during various sub-periods, but not for all periods.
Furthermore, these periods coincide with major changes in the oil and stock markets and
geopolitics, implying that the empirical findings are not statistical artifacts, but reflect real
economic, financial and geopolitical regime changes. The full sample’s conditional Granger
causality tests based on the Markov switching vector-error correction (MS-VEC) model reject
both the causal impact of the lagged futures prices on the spot prices, and the causal impact
from the lagged spot prices to the futures prices. Therefore, the results show that the lead-lag
relationship between the spot and futures oil markets exists only temporarily. The results
encompass existing empirical results and offer new insights into the nature of the lead-lag
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relationships between the spot and futures oil markets by accounting for non-linearity and time
variation.
The fifth paper by Mehmet Balcilar (Department of Economics, Eastern Mediterranean
University, Turkey), Shawkat Hammoudeh (Lebow College of Business, Drexel University,
USA), and Nwin-Anefo Fru Asaba (Department of Economics, Eastern Mediterranean
University, Turkey) is entitled “A regime-dependent assessment of the information
transmission dynamics between oil prices, precious metal prices and exchange rates”. The
authors use the Bayesian Markov-Switching Vector Error Correction (MSVEC) model and the
regime-dependent impulse response functions (RDIRF) to examine the transmission dynamics
of oil spot prices, precious metals (gold, silver, platinum, and palladium) spot prices and the
US dollar/euro exchange rate. Using daily data from 1987 to 2012, two regimes (namely, low
and high volatility regimes) seem to be suitable empirically. The authors find that gold prices
are the most informative in the high volatility regime, while gold, palladium, and platinum are
the most informative in the low volatility regime. While the platinum and palladium prices
impact each other, the impacts in the high volatility regime are asymmetric. In addition to its
low correlation in the group, the negative impact of palladium on the exchange rate and gold
makes it a reliable hedge asset for investors. Gold is the least volatile variable, thereby
affirming its use as a “safe haven” asset, while silver and oil are the most volatile in the
group. Understanding the dynamics of these commodity prices should help investors decide
how to invest during periods of low and highly volatility regimes.
“A practical approach to constructing price-based funding liquidity factors” is the sixth paper,
by Kees Bouwman (Cardano Risk Management, The Netherlands), Boyd Buis (Econometric
Institute, Erasmus University Rotterdam, The Netherlands, KAS Bank), Mary Pieterse-
Bloem (Econometric Institute, Erasmus University Rotterdam, The Netherlands, APG Asset
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Management), and Wing Wah Tham (Econometric Institute, Erasmus University Rotterdam,
and Tinbergen Institute, The Netherlands). The paper proposes a computationally convenient
and parsimonious approach for creating a funding liquidity factor, building on work that relates
the funding constraints of financial institutions to their ability to exploit arbitrage opportunities.
By studying mispricing among bonds of similar characteristics but of different ages, the
Fontaine-Garcia approach uses an arbitrage-free Nelson-Siegel framework to construct a price-
based liquidity factor. However, this requires the use of a non-linear Kalman filter, which is
computationally intensive in practice. The novelty of the paper is to suggest an easier method
for constructing an alternative liquidity factor that retains much of the same properties. The
authors construct an alternative liquidity factor estimate by relaxing the arbitrage-free
assumption in the specification of the term structure model, and base it on a simple and flexible
term structure specification. They demonstrate that this parsimonious liquidity factor fits the
data well. The constructed factor is highly correlated with the Fontaine-Garcia liquidity factor
and other funding liquidity measures, such as the liquidity factor, the TED-spread and the CP-
spread.
Massimiliano Caporin (Department of Economics and Management “Marco Fanno”,
University of Padova, Italy) and Gabriel Velo (Department of Economics and Management
“Marco Fanno”, University of Padova, Italy) present the seventh paper, namely “Realized
range volatility forecasting: Dynamic features and predictive variables”. The authors estimate,
model and forecast Realized Range Volatility, a realized measure and estimator of the
quadratic variation of financial prices. The realized range was developed very early in the
literature and is based on the high-low range observed at high frequency during the day. The
paper considers the impact of the microstructure noise in high frequency data and corrects the
estimates based on a standard procedure. The authors model the Realized Range and account
for the well-known stylized effects that are present in financial data, and investigate the role
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that macroeconomic and financial variables play when forecasting daily stock volatility.
They consider a Heterogeneous Autoregressive (HAR) model with asymmetric effects with
respect to the volatility and returns, and GARCH and GJR specifications for the conditional
variance equation. Moreover, the authors consider a non-Gaussian distribution for the
innovations. Finally, the paper extends the model by including macroeconomic and financial
variables that capture the current and future states of the economy. It is found that these
variables are significantly correlated with the first common component of the volatility series
and have high in-sample explanatory power. The analysis of the forecast performance of 16
NYSE stocks suggests that the introduction of asymmetric effects with respect to the returns
and volatility in the HAR model result in significant improvements in the point forecasting
accuracy, as well as the variables related to the U.S. stock market performance and proxies for
the credit risk.
The eighth paper, “Modelling a latent daily tourism financial conditions index”, is by Chia-Lin
Chang (Department of Applied Economics and Department of Finance, National Chung Hsing
University, Taiwan). The paper uses daily data on financial stock index returns, tourism stock
sub-index returns, exchange rate returns and interest rate differences from June 1, 2001
to February 28, 2014 for Taiwan to construct a novel latent daily tourism financial
indicator, namely the Tourism Financial Conditions Index (TFCI). The TFCI is an
adaptation and extension of the widely-used Monetary Conditions Index (MCI) and
Financial Conditions Index (FCI) to tourism stock data. However, the method of calculation
of the daily TFCI is different from existing methods of constructing the MCI and FCI in that
the weights are estimated empirically. Alternative versions of the TFCI are constructed,
depending on the appropriate model and method of estimation, namely Ordinary Least Squares
(OLS) or Quasi-Maximum Likelihood Estimation (QMLE) of alternative conditional volatility
models. Three univariate conditional volatility models are considered, namely GARCH, GJR
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and EGARCH, in an attempt to capture the inherent volatility in the daily tourism stock index
returns. The empirical findings show that TFCI is estimated quite accurately using the
estimated conditional mean of the tourism stock index returns, especially when conditional
volatility is incorporated in the overall specification. The new daily TFCI is straightforward to
use and interpret, and provides interesting insights in predicting the current economic and
financial environment for tourism stock index returns, especially as it is based
on straightforward calculations and interpretations of publicly available information.
“Bank ownership, financial segments and the measurement of systemic risk: An application of
CoVaR”, is the ninth paper, by Anastassios A. Drakos (Department of Business
Administration, Athens University of Economics and Business, Greece) and Georgios P.
Kouretas (Department of Business Administration, Athens University of Economics and
Business, Greece). The recent financial crisis has shown that the regulatory framework that has
been formulated and implemented over the last twenty years under the Basel I and II
agreements has relied excessively on the monitoring of individual financial institutions. It
failed to capture the contribution of systemic risk, which is considered to be the risk that is the
outcome of collective behaviour of financial institutions that have significant effects on the real
economy. This paper investigates whether the increased presence of foreign banks which are
listed on a national stock market has contributed to the increase in the systemic risk,
in particular, after the global financial crisis of 2007-2009. The authors examine the extent to
which the distress of foreign banks contributes to systemic risk for the USA. In addition, using
relevant data for the UK, they investigate the extent to which distress within different sub-
segments of the financial system, namely the banking, insurance and other financial services
industries, contribute to systemic risk. The analysis is conducted using the recently developed
CoVaR measure of systemic risk using daily data for the period 2 January 2000 to 31
December 2012. Furthermore, the authors complement their analysis with the application of
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two tests, the significance and dominance tests, to provide a formal comparison of the relative
contribution of either the domestic or foreign banks and/or each individual financial
sector. The main results provide evidence that in the USA, the non-US banks contribute to the
systemic risk, although most of the contribution comes from the US banks. In the case of the
UK, the authors find that the banking industry contributes relatively more to systemic risk in
periods of distress than the insurance industry or the other financial services
industry. Furthermore, when they examine the estimated change in CoVaR measures, it is
observed that the contribution to systemic risk has increased since 2008 for all sectors.
Maria Teresa Gonzalez-Perez (Colegio Universitario de Estudios Financieros (CUNEF),
Madrid, Spain) presents the tenth paper on “Model-free volatility indexes in the financial
literature: A review”. The paper describes the primary uses of the VIX index in the financial
literature, offering for the first time a joint view of the successes and failures of VIX in key
financial areas. VIX is a model-free volatility index that measures the investor “fear” due to its
significant and negative relationship with the S&P 500 returns dynamics, which justifies its use
as a proxy for market risk and volatility. Of all the uses made of VIX, this article focuses on
the most frequent, namely as: (1) a financial product to hedge a portfolio against volatility risk;
(2) a market risk measure used to analyze risk flows from financial markets, and relate private
and public risks; and (3) a volatility measure to use in the estimation of the spot volatility
dynamics, the volatility risk premium and volatility jumps. The current literature continues to
use VIX in a similar way. The paper offers an introductionfor researchers who consider VIX as
a proxy for volatility and/or risk.
In the eleventh paper, “Robust hedging performance and volatility risk in option
markets: Application to Standard and Poor’s 500 and Taiwan index options”, Chuan‐Hsiang
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Han (Department of Quantitative Finance, National Tsing Hua University, Taiwan), Chien-
Hung Chang (Department of Financial and Computational Mathematics, Providence
University, Taiwan), Chii‐Shyan Kuo (Department of Accountancy, National Cheng Kung
University, Taiwan), and Shih‐Ti Yu (Department of Quantitative Finance, National Tsing Hua
University, Taiwan) investigate the daily robust hedging performance with trading costs for
both the S&P 500 Index option (SPX) and Taiwan Index option (TXO) markets. A theoretical
analysis is presented to cope with the price limit constraint in TXO. Robust hedging refers
to minimal model dependence on the risky asset price. Two hedging categories, including
“model-free” and “volatility-model-free,” are investigated, and nonparametric methods for
volatility estimation are considered in the empirical analysis. In particular, the
instantaneous volatility is estimated by a novel nonlinear correction scheme of the
Fourier transform method, which is justified in a simulation study for a local volatility
model. An asymmetric phenomenon of hedging performance is found. Hedging portfolios
constructed from the “volatility-model-free” category are found to induce much higher Sharpe
ratios than those from the “model-free” category on SPX, while they are found to perform
comparably on TXO. Owing to price limit regulations in Taiwan, the paper also develops a
time-scale change method to explain this phenomenon. The asymptotic moment estimates of
differences in some hedging portfolios are found to be consistent with the empirical findings.
The twelfth paper on “Price cointegration between sovereign CDS and currency option
markets in the financial crises of 2007-2013”, is by Cho-Hoi Hui (Research Department, Hong
Kong Monetary Authority, Hong Kong, China) and Tom Pak-Wing Fong (Research
Department, Hong Kong Monetary Authority, Hong Kong, China). The sovereign credit
default swap (CDS) spreads and exchange rates of developed economies, including the USA,
Japan, Switzerland and the Eurozone, with the first three countries’ currencies conventionally
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considered as safe havens, have varied over a wide range during the financial crises since late-
2007. This raises the question as to any interconnectivity between the anticipated sovereign
credit risks of these economies and the market expectations of their exchange rates. Using a
bivariate vector error-correction model with random coefficients, the authors find evidence of
cointegration and time-varying conditional correlation between the prices in the sovereign CDS
and currency option markets. The empirical results show that the relative sovereign credit risks
of these developed economies impacts the market expectations of their exchange rates in the
long run. In the short run, the impact changes drastically in times of crisis, resulting in drastic
and persistent price deviations from their long-run equilibrium amid monetary measures by
central banks and market turbulence.
Marcin Jaskowski (Econometric Institute, Erasmus School of Economics, Erasmus University
Rotterdam, The Netherlands) asks and answers the question “Should zombie lending always be
prevented?” in the thirteenth paper. It has been argued that zombie lending might have been
one of the main culprits behind the sluggish Japanese recovery in the so-called “lost decade”.
Among others, zombie lending may lead to misallocation of capital, reduction of profits for
healthy firms, and lower employment. The only remaining question is: Why do banks engage
in zombie lending practices? Is it due to wrong incentives for bank managers, or perhaps
misguided government policies? Using a simple model, the paper exposes a strong link
between collateral value and the strategic importance of zombie lending. The author shows that
zombie lending may be an optimal strategy for a bank in some cases as it leads to greater
lending ex-ante and prevents further losses from fire sales. Consequently, it can be argued that
zombie lending is a side effect of market incompleteness and is ex ante welfare improving, so
that it may not be possible or even desirable to prevent its occurrence. Another policy
implication is that capital injection into banks would not solve the problem. However, direct
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purchases of the collateral on the market would certainly alleviate the problem of zombie
lending.
In the fourteenth paper, “Preferences of risk-averse and risk-seeking investors for oil spot and
futures before, during and after the global financial crisis”, Hooi Hooi Lean (Economics
Program, School of Social Sciences, University Sains Malaysia), Michael
McAleer (Department of Quantitative Finance, National Tsing Hua University,
Taiwan; Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam;
Tinbergen Institute, The Netherlands), and Wing-Keung Wong (Department of Economics,
Hong Kong Baptist University, Hong Kong, China) examine risk-averse and risk-seeking
investor preferences for oil spot and futures prices before, during and after the global financial
crisis by using the mean-variance (MV) criterion, the CAPM statistics, and stochastic
dominance (SD) approach. The MV criterion shows that risk averters are indifferent between
oil spot and futures prices, but risk seekers prefer to invest in oil futures rather than spot prices.
The information drawn from the CAPM statistics does not lead to any preference between oil
spot and futures prices. The SD tests show that risk-averse investors prefer the spot index,
whereas risk seekers are attracted to the futures index to maximize expected utility, though not
expected wealth for the entire period, as well as for the sub-period before the 2008 Global
Financial Crisis (GFC) and the sub-periods during and after the GFC. In order to compare the
performance of spot prices in the pre-GFC and GFC sub-periods, it is found that the 2008 GFC
has no impact on the means and variances, or the CAPM statistics. Moreover, the SD analysis
reveals that spot prices from the pre-GFC and GFC sub-periods do not dominate each other,
both risk averters and risk seekers are indifferent between the spot prices from the pre-GFC
and GFC sub-periods, there is no arbitrage opportunity for spot prices before and after the
GFC, and the spot market is efficient with respect to the GFC crisis. A similar conclusion is
drawn regarding the impact of the GFC on the oil futures prices. The empirical findings in the
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paper provide useful information for academics, practitioners, and policy makers in their
decisions regarding oil spot and futures markets, as well as towards the likely impacts of any
significant financial crisis in the future.
Johnny S.H. Li (Department of Statistics and Actuarial Science, University of Waterloo,
Canada), Andrew. C.Y. Ng (Department of Finance, Chinese University of Hong Kong, Hong
Kong, China), and Wai-Sum Chan (Department of Finance, Chinese University of Hong Kong,
Hong Kong, China) examine “Managing financial risk in Chinese stock markets: Option
pricing and modeling under a multivariate threshold autoregression” in the fifteenth paper. The
Shanghai Stock Exchange and the Shenzhen Stock Exchange in Mainland China have grown
remarkably since their inception 20 years ago. Many investors in these two markets are asset
management firms or pension funds, some of which may offer guaranteed returns to their
clients. To these investors, modeling and managing the risk associated with their equity
investments are very important. The authors use a multivariate Threshold AutoRegressive
(TAR) process to model the non-linear relationship between these two markets. The model
may help fund managers better plan or execute their risk management decisions as it
captures the difference in behaviour of investment returns when one market significantly out-
or under-performs the other. The authors also develop a risk-neutral version of the multivariate
TAR model. This contribution permits price exotic options to be written on multiple stock
indexes, and consequently helps fund managers calculate the cost of an option-based risk
management strategy for funds involving the two Chinese markets.
“Managing systemic risk in The Netherlands” is the sixteenth paper, by Shuyu Liao (Erasmus
University Rotterdam, The Netherlands), Elvira Sojli (Rotterdam School of Management,
Erasmus University Rotterdam, The Netherlands), and Wing Wah Tham (Econometric
Institute, Erasmus University Rotterdam, and Tinbergen Institute, The Netherlands). The paper
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investigates the effects on systemic risk of macroprudential capital requirements, which require
banks to hold capital that is proportional to the bank’s contribution to systemic risk. The
authors use a panel of correlated Merton balance sheet models, combined with a network
clearing algorithm, to measure systemic risk and how it changes with bank capital. The model
explicitly incorporates the possibility of default through common exposures to macroeconomic
factors and interbank exposures. They use five risk allocation mechanisms to allocate systemic
risk to individual banks. Using a sample of Dutch banks, the authors find that the
macroprudential capital requirements deviate from the current observed capital levels by as
much as 40%, and are positively related to bank size and interbank exposure. Furthermore, it is
found that macroprudential capital requirements can reduce individual and multiple banks
default probabilities by up to 26%, and are robust to model risk. The results suggest that
financial stability can be substantially improved by implementing macroprudential regulations
for the banking system.
Gustavo A. Marrero (Department of Economics, Universidad de La Laguna, Tenerife,
Spain), Luis A. Puch (Department of Economics, Complutense University of Madrid,
Spain), Francisco J. Ramos-Real (Department of Economics, Universidad de La Laguna,
Tenerife, Spain) consider “Mean-variance portfolio methods for energy policy risk
management” in the seventeenth paper. The risks associated with the current and prospective
costs of different energy technologies are crucial in assessing the efficiency of the energy mix.
However, energy policy typically relies on the evolution of average costs, neglecting the
covariances in the costs of the different energy technologies in the mix. In the paper, Mean-
Variance Portfolio Theory is implemented to evaluate jointly the average costs and the
associated volatility of alternative energy combinations. Based on a Capital Asset Pricing
Model with time-varying betas, the systematic and non-systematic risks associated with energy
technologies are computed. The authors show that both electricity generation and fuel use
18
imply risks that are idiosyncratic, with relevant implications for energy and environmental
policy.
In the eighteenth paper, Hiroumi Misaki (Research Center for Advanced Science and
Technology, University of Tokyo, Japan) and Naoto Kunitomo (Graduate School of
Economics, University of Tokyo, Japan) investigate issues associated with “On robust
properties of the SIML estimation of volatility under micro-market noise and random
sampling”. For estimating the integrated volatility and covariance by using high frequency
data, the Separating Information Maximum Likelihood (SIML) method in the presence of
micro-market noise has been proposed in the literature. The SIML estimator has reasonable
finite sample properties and asymptotic properties when the sample size is large, under general
conditions with non-Gaussian processes or volatility models. The paper shows that the SIML
estimator is asymptotically robust in the sense that it is consistent and has stable convergence
(that is, asymptotic normality in the deterministic case), as well as reasonable finite sample
properties, when there is micro-market noise and the observed high frequency data are sampled
randomly with the underlying (continuous time) stochastic process. The authors also discuss
some implications of the results on public policy and risk management in financial markets.
“ALRIGHT: Asymmetric LaRge-Scale (I)GARCH with Hetero-Tails” is the nineteenth paper,
by Marc S. Paolella (Department of Banking and Finance, University of Zurich, Switzerland)
and Pawel Polak (Department of Banking and Finance, University of Zurich, Switzerland). It is
well known in empirical finance that virtually all asset returns, whether monthly, daily, or
intraday, are heavy-tailed and, particularly for stock returns, are mildly but often significantly
negatively skewed. However, the tail indices, or maximally existing moments of the returns,
can differ markedly across assets. In order to accommodate these stylized facts when modeling
the joint distribution of asset returns, an asymmetric extension of the meta-elliptical t
19
distribution is proposed. While the likelihood is tractable, for high dimensions it will be
impractical to use for estimation. In order to address this issue, a fast, two-step estimation
procedure is developed, based on a saddlepoint approximation to the noncentral Student t
distribution. The model is extended to support a CCC-(I)GARCH structure, and is
demonstrated by modeling and forecasting the return series comprising the DJIA. The
techniques of shrinkage, time-varying tail dependence, and weighted likelihood are used to
enhance the forecasting performance of the model with no added computational burden.
In the twentieth paper, entitled “The economic fundamentals and economic policy uncertainty
of Mainland China and their impacts on Taiwan and Hong Kong”, Chor-yiu (CY)
Sin (Department of Economics, National Tsing Hua University, Taiwan) 0bserves that the
opening-up of Mainland China has significantly increased its economic relationship with
Taiwan and Hong Kong. Trade among Taiwan, Hong Kong and Mainland China has registered
record highs in recent years, and capital flow has grown exponentially. The paper applies
structural vector autoregressive (SVAR) models to the Taiwan and Hong Kong economies to
investigate the impacts of the Chinese economy over the past decade. Identification of the
structural shocks is based on the fact that Mainland China has a causal effect on Taiwan and
Hong Kong, but not the reverse. The identification scheme generalizes the Blanchard and Quah
procedure, and considers both short-run and long-run restrictions. Based on a New Keynesian
model, a simple model with four domestic variables, namely output, interest rate, price level
and real exchange rate, as well as two foreign variables, namely Mainland China’s output and
the China monthly index of economic policy uncertainty, is constructed. By considering the
short-run and long-run impacts, the paper addresses whether domestic output or some other
domestic variables are affected by the uncertain economic conditions in the short or long run.
20
The penultimate paper by Norman R. Swanson (Department of Economics, Rutgers University,
USA), Richard Urbach (Conning Germany GmbH), is entitled “Prediction and simulation
using simple models characterized by nonstationarity and seasonality”. The paper provides
new evidence on the empirical usefulness of various simple seasonal models, and underscores
the importance of carefully designing criteria by which to judge alternative models. In
particular, the authors underscore the importance of both choice of forecast or simulation
horizon and choice between minimizing point or distribution-based loss measures. The
empirical analysis centers around the implementation of a series of simulation and prediction
experiments, as well as a discussion of the stochastic properties of seasonal unit root models.
The prediction experiments are based on an analysis of a group of 14 variables that have been
chosen to mimic closely the set of indicators used by the Federal Reserve to help in setting
U.S. monetary policy. The simulation experiments are based on a comparison of simulated and
historical distributions of the variables. A key impetus for the paper stems from the fact that
various financial service companies routinely create “economic scenarios”, whereby seasonal
and nonstationary financial and economic variables are simulated (and predicted) using
relatively simple time series models. These “economic scenarios” are subsequently used in risk
management and asset allocation, as is often mandated by various world financial regulatory
authorities. The empirical findings suggest that a simple version of the seasonal unit root
(SUROOT) model performs very well for predicting 8 of 14 variables, when the forecast
horizon is 1-step ahead. However, for horizons of greater than one-step ahead, the SUROOT
model performs poorly when used for prediction, suggesting that parameter estimation error is
crucial to understanding the empirical performance of such models. This “parameter estimation
error” result is confirmed via a series of Monte Carlo experiments. Simulation experiments
yield similar conclusions, although SUROOT models in this case are useful for constructing
“forward” conditional distributions at 1- and 3-step ahead horizons. Interestingly, simple
21
periodic autoregressions do not have this property, and are found to perform very well in both
prediction and simulation experiments, at all horizons up to 60 months ahead.
The final paper, entitled “Volatility forecast of stock indexes by model averaging using high
frequency data”, is by Chengyang Wang (Graduate School of Economics, Kyoto University,
Japan) and Yoshihiko Nishiyama (Institute of Economic Research, Kyoto University, Japan).
Models from the GARCH class perform well in the analysis of volatility forecasts. The authors
use the realized GARCH (RGARCH) model, high frequency based volatility (HEAVY) model,
and multiplicative error model (MEM) to evaluate the performance of the one-day ahead
volatility forecasts of Chinese and Japanese stock indices. They propose to combine the models
by the model averaging technique to explore the possibility of obtaining better predictions. The
CSI 300 and Nikkei 225 indexes are used in the empirical analysis. The authors use rolling
estimation and predict the daily volatilities. They evaluate the forecast performance by the
superior predictive ability (SPA) test. As a result, they find that combination methods
significantly improve the forecast performance compared with the best single model
predictions.
3. Final remarks
The collection of interesting, timely and novel papers in this special issue by some of the
leading experts in the field of “Advances in Financial Risk Management and Economic Policy
Uncertainty” should both highlight and encourage innovative research in a variety of
challenging areas associated with the topical and rapidly expanding areas of financial risk
management and economic policy uncertainty.
22
It is our pleasure to acknowledge all the contributors for preparing their invaluable, interesting
and innovative papers in a timely manner, and for their willingness to participate in the
rigorous editorial review process.
23
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